Our approach consists of following steps: (1) parse original protocol into a collection of protocol phrases together with their procedural relations, using a deterministic finite automation (DFA); (2) Match the protocol phrases back to the text spans in transcripts using fuzzy matching [1]}, [2]}; (3) Generate text span extraction dataset and train a sequence labeling model [3]}, [4]} for text span extraction; (4) Generate text span-pair relation extraction (span-pair RE) dataset and fine-tune pre-trained context-aware span-pair RE model [2]}. With the trained models, we can automatically extract text spans summarizing actions from transcripts along with the procedural relations among them. Finally, we assemble the results into protocol knowledge, which lays the foundation for CTA.
